The electrolytic reduction of alumina is normally carried out in a Hall-Heroult cell which comprises an elongated, shallow vessel lined with a conductor material, such as carbon, forming a cathode. The vessel holds a molten electrolyte, typically cryolite, containing a low concentration of dissolved alumina, and a number of carbon anodes dipped into the electrolyte from above. When a direct current is passed through the cell, molten aluminum is formed and descends to the bottom of the cell where it forms a pool acting as part of the cell cathode.
As electrolysis proceeds, the concentration of alumina in the electrolyte falls and more alumina is added periodically. When the concentration in these regions falls to about 2% by weight or less, the so-called “anode effect” is observed. This manifests itself as a high voltage and the appearance of fluorocarbons in the anode gases. The anode effect is disadvantage for a number of well known reasons and attempts are made to avoid and/or terminate the anode effect.
It would be highly desirable to be able to directly measure the alumina content of electrolysis cells. It is known to remove a sample from the electrolysis cell and analyze it for alumina content, but this is too slow to be commercially practical. Thus, most industrial processes have resorted to indirect evaluation of the alumina content by following an electrical parameter representative of the alumina concentration of the electrolyte. This parameter is generally a variation of the resistance at the cell electrode terminals such as a resistance trend (a.k.a. R-trend) indicator. The curve of the variation of resistance as a function of alumina content can be plotted by calibration and the alumina concentration can thereby be known. However, the measured resistance can be affected by factors other than the alumina concentration such as for example environmental (e.g. electrical) interference, perturbation generating phenomena, and other sources of noise which can be difficult to control or eliminate.
Digital filters are used in a wide range of domains for signal processing and conditioning. In particular a cascade of multiple filters, such as Kalman filters, can be used for smoothing of a signal to remove or minimize the noise content in a measured signal. The individual filter parameters can be chosen, for example, based on observations made in the time domain.
Digital filters have been used in the control of reduction cells to mitigate the impact of extraneous factors, such as those described above, on the resistance trend indicator. As the selection of filter parameters has typically been based on observations in the time domain, the impact of the filters in terms of attenuation as a function of frequency and other related performance characteristic is not generally known in these applications. Therefore, it has not been possible to predict the performance of the filters in the face of different phenomena (e.g. bubble noise, metal waves) that can occur in the reduction cell and affect measurement signals.
What is needed is a method for designing a cascade of digital filters for use in controlling an electrolytic reduction cell that permits the performance characteristic (e.g. frequency response, stabilization time, group delay) of the cascade of filters to be adapted in light of knowledge of various types of phenomena that can occur in the cell.